Abstract

BACKGROUND:

The speed at which biological datasets are being accumulated stands in contrast to our ability to integrate them meaningfully. Large-scale biological databases containing datasets of genes, proteins, cells, organs, and diseases are being created but they are not connected. Integration of these vast but heterogeneous sources of information will allow the systematic and comprehensive analysis of molecular and clinical datasets, spanning hundreds of dimensions and thousands of individuals. This integration is essential to capitalize on the value of current and future molecular- and cellular-level data on humans to gain novel insights about health and disease.

RESULTS:

We describe a new open-source Cytoscape plugin named iCTNet (integrated Complex Traits Networks). iCTNet integrates several data sources to allow automated and systematic creation of networks with up to five layers of omics information: phenotype-SNP association, protein-protein interaction, disease-tissue, tissue-gene, and drug-gene relationships. It facilitates the generation of general or specific network views with diverse options for more than 200 diseases. Built-in tools are provided to prioritize candidate genes and create modules of specific phenotypes.

CONCLUSIONS:

iCTNet provides a user-friendly interface to search, integrate, visualize, and analyze genome-scale biological networks for human complex traits. We argue this tool is a key instrument that facilitates systematic integration of disparate large-scale data through network visualization, ultimately allowing the identification of disease similarities and the design of novel therapeutic approaches.The online database and Cytoscape plugin are freely available for academic use at: http://www.cs.queensu.ca/ictnet.

Screenshot of iCTNet. Genetic association data for more than 200 traits and diseases are available to download from the iCTNet database at a user-selectable significance threshold (-Log10(p)). In addition, the user can choose to download disease-tissue, tissue-gene, and drug-gene interactions by simply ticking a checkbox. Protein-protein and protein-DNA interactions can also be downloaded at different degrees of separation (ds) or distance. Choosing a distance ds = 0 only downloads direct disease-gene associations, and any existing interaction among associated gene products (protein-protein). A distance ds = 1 will also include the first neighbors of genes directly associated.

A network of five common autoimmune diseases. A. Disease-gene interaction network (ds = 0) for five common autoimmune diseases. Each disease has unique and shared associations. RA, T1D, and MS are closely related both through HLA and non-HLA associated genes. B. A simplified version of the network shown in A, using the "create similarity net" feature of iCTNet. In this representation, diseases are connected by edges of a color proportional to the number of shared genes. C. Same network as in A with drug-target interactions. Colored circles represent diseases (MS = yellow, T1D = red, RA = green, Ps = magenta, CD = teal), white triangles represent genes, and cyan round squares represent drugs. Disease-gene interactions are colored according to the disease. Protein-protein and DNA-protein interactions are shown as white edges. Drug-gene interactions are represented as cyan dashed edges.